Learning occurs over time and interest in analyses that probe temporal aspects continues to grow as a powerful way to understand the processes through which learning occurs. The study of common and consequential sequences of events (such as learners accessing resources, interacting with other learners and engaging in self-regulatory activities) and how these associate with learning outcomes, as well as the ways in which knowledge and skills grow or evolve over time are both core areas of interest. However, the emerging area of temporal analysis presents both technical and theoretical challenges in appropriating suitable techniques and interpreting results in the context of learning. The learning analytics community offers a productive focal ground for exploring and furthering efforts to address these challenges as it is already positioned in the "‘middle space' where learning and analytic concerns meet (Suthers & Verbert, 2013, p 1). In addition learning analytics datasets are replete with fine-grained temporal data: click streams; chat logs; document edit histories (e.g. wikis, etherpads); motion tracking (e.g. eye-tracking, Microsoft Kinect), and so on. This workshop, the fourth in a series on temporal analysis of learning data, provides a focal point for analytics researchers to consider the larger issues of and approaches to temporality in learning analytics. The focus is on the particular opportunities and challenges of temporal analysis for learning analytics and how learning analytics data, methods and approaches can inform the larger community working with temporal analyses of learning data.

To Participate:

Those interested in participating are invited to submit short (<1 page) applications for one of the following roles briefly describing which role you would like to participate in at the workshop and what you bring to the position (past experience working with temporal analyses or how this area relates to your research interests). Those proposing a conceptual, data, or analytic presentation should also briefly outline what they would like to present on - i.e. conceptual issues addressed / characteristics of the data set and if any, analyses conducted thus far / analytic approach and what data it is suitable for: Conceptual presenters – those who submit short conceptual papers on temporality for discussion at the workshop Data presenters – those who provide a salient dataset for discussion of its temporal properties and potential analyses Analytic presenters – those who provide an analytic technique relevant to temporal analysis Commentators – those who commit to reading, and responding to, at least one of the above kinds of submission – note that we welcome commentators with all backgrounds and levels of experience Short applications to participate should be emailed to sjgknight@gmail.com with the subject line ‘LAKTime' by January 11th

We aim to send out notifications on January 16th, before the earlybird deadline (Jan 21st)

We aim to understand ethics and privacy issues in learning analytics with greater clarity, to find ways of overcoming these issues and to research challenges related to ethical and privacy aspects of learning analytics practice. This interactive workshop aims to raise awareness of major ethics and privacy issues. It will also be used to develop practical solutions for learning analytics researchers and practitioners that will enable them to advance the application of learning analytics technologies.

Description: This workshop is focused on the analysis graph or relational data such as social networks among peers, user-system interaction data, student paths through course materials, and student solutions structured as graphs. As such it is intended for researchers, instructors, and developers who have relational data and wish to analyze it, and those who have developed graph analysis techniques such as hierarchical models that they wish to apply to graphical data. It is our hope that these two groups will both attend the workshop and benefit from the enhanced collaboration.

Description: Open Badges (OBs) connect educational providers and practitioners, entrepreneurs, and researchers in discourses on teaching, learning, assessment, digital credentials, and digital education in general. While preserving its overall focus on opportunities and challenges associated with OBs, this 2nd installment of the OBIE workshop will be primarily intended for those interested in the intersection of OBs and Learning Analytics. This intersection includes gathering, integration, and analysis of data and resources associated with OBs, with the ultimate aim of providing teachers, learners and other stakeholders in the ever-increasing OBs ecosystem with informative and relevant feedback, and predictive functions. Participants in the workshop will be exposed to presentations and discussions on the position and role of digital badges in instructional design, on compelling data and analytics challenges and opportunities that the OB infrastructures open for the Learning Analytics field, and on different types and designs of OB systems and their potential to impact the future of education.

The workshop is intended for anyone who is using, or is interested in visualization techniques to support learning analytics. The goal of our workshop is to build a strong research capacity around visual approaches to learning analytics. The longer term goal is to improve the quality of learning analytics research that relies on information visualization techniques.

The workshop is explicitly aimed at participants from a range of research fields and expertises. Authors from diverse fields, like pedagogy, information visualization, visual analytics, psychology, cognitive science, etc. are encouraged to submit their work and participate in the workshop. There will be an opportunity to present concepts and approaches, but also algorithms and implementations for discussion and feedback.

The workshop will enable researchers to get acquainted with related research fields, enabling in depth studies and contacts and thereby fostering the discussion of research issues around the area of visual approaches to learning analytics.

Description: This tutorial is designed for learning analytics researchers with social science backgrounds, and it does not expect any prior knowledge with topic modeling or R programming language. Only very basic knowledge of statistics (e.g., what is population, sample, test statistic) and linear algebra (e.g., basics of vectors and matrices) are assumed. Tutorial will also be useful for learning analytics researchers with computer science or data mining backgrounds, particularly the ones without experience with topic modeling techniques.

Description: Bayesian Knowledge Tracing has been at the fore in assessing student changing knowledge in digital learning environments. While originally put into practice in Intelligent Tutoring Systems in order to assess student skill mastery, it's uses now include evaluation of item diagnostic power, and learning properties of various interventions and patterns of use. In this tutorial I will introduce BKT as it is current used in practice, the basics of the algorithm, and a hands-on tutorial of how to apply BKT analysis to a dataset to assess individual student knowledge and test learning hypotheses. The tutorial will utilize Kevin Murphy's Bayes Net Toolbox which is compatible with Octave, a free alternative to MATLAB.

Day 2 - Afternoon (NOTE: This session has been combined with the afternoon session of the VISLA15 workshop (see above).

Description:

The LAK Dataset provides access to structured metadata from research publications in the field of learning analytics. Beyond merely publishing the data, we are actively encouraging innovative use and exploitation of it as part of a public LAK Data Challenge sponsored with $750 by the Society of Learning Analytics (SoLAR). Challenge submissions should exploit the LAK Dataset (http://lak.linkededucation.org) for a meaningful purpose.